DSA is a fabrication approach that uses the self-assembly properties of block copolymer materials to form patterning structures for use in semiconductor device fabrication. The DSA of block copolymers is a promising technology to extend patterning resolution for next generation technology nodes, e.g., 10 nm and beyond. In general, DSA is based on the phase separation of block copolymers to generate, e.g., dense grating structures with nanoscale feature sizes and pitches. The grating structures comprise lamellar structures composed of alternating layers of different materials in the form of lamellae. The phase separation of block copolymer material can be facilitated by the use of chemically and/or physically pre-patterned guiding structures. One of the major challenges for the use of DSA as a viable lithography solution is the ability to implement defect-free DSA processes. The morphological defects of block copolymer materials are dynamically formed during an annealing process which is implemented to cause phase separation and self-assembly of the constituent block copolymer materials to form a target DSA structure. Understanding the dynamics of such defects is therefore useful in order to manufacture defect-free wafers.
In this regard, various computer simulation techniques have been implemented to predict phase-separated morphologies of block copolymers on chemically and physically pre-patterned guiding surfaces and structures. However, many conventional DSA simulation techniques are computationally expensive and/or not sufficiently accurate for large-scale DSA simulations. For example, many physical models for DSA require accurate calibration of model parameters from actual wafer data to make an accurate prediction. Most of the calibration is based on morphology data at a single process condition but this will sacrifice model accuracy. In addition, some DSA modeling techniques enhance the calibration steps by using kinetic data (time stepping) for more accurate modeling of the process parameters, but such modeling is computationally complex as it requires solving the DSA model equations dynamically (via partial differential equations) by finite difference (or element) time domain to generate the intermediate time stepping simulation data. The kinetic data comprises information regarding, e.g., the formation of DSA defects as a function of anneal time. Typically, the time-domain based DSA models utilize a stochastic noise term in the equation to help convergence since without noise, free energy always decreases with time with the risk of being trapped in a metastable state. Typically, these conventional time-domain based approaches have various disadvantages including, but not limited to, slow convergence, long computation times, unstable for large values of certain parameters in the applied solution, sensitive to initial random state and noise, etc.